Evidence map›Paper›PMID 28391525›Full record

ArticleHuman genetics2017

regSNPs-splicing: a tool for prioritizing synonymous single-nucleotide substitution.

Xinjun Zhang, Meng Li, Hai Lin, Xi Rao, Weixing Feng, Yuedong Yang, Matthew Mort, David N Cooper, Yue Wang, Yadong Wang and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Human genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.

  1. Pooled it
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  3. Article
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  5. Article
  6. Review
  7. Article
  8. Article
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  12. Review
  13. Review
  14. Review
  15. Article
  16. Decoding the effects of synonymous variants.Nucleic acids research · 2021
    Article
  17. Article
  18. Article
  19. Article
  20. Genomic Variation Prediction: A Summary From Different Views.Frontiers in cell and developmental biology · 2021
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 7 institutions in 4 countries.

Xinjun ZhangSchool of Informatics and Computing, Indiana University, Bloomington, IN, 47408, USA.
Meng LiCenter for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 w 10th st, HS 5000, Indianapolis, IN, 46202, USA.
Hai LinCenter for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 w 10th st, HS 5000, Indianapolis, IN, 46202, USA.
Xi RaoCenter for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 w 10th st, HS 5000, Indianapolis, IN, 46202, USA.
Weixing FengPattern Recognition and Intelligent System Institute, Automation College, Harbin Engineering University, Harbin, 150001, Heilongjiang, China.
Yuedong YangSchool of Information and Communication Technology, Gold Coast Campus, Griffith University, Southport, QLD, 4222, Australia.
Matthew MortInstitute of Medical Genetics, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK.
David N CooperInstitute of Medical Genetics, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK.
Yue WangDepartments of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Yadong WangSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China.
Clark WellsDepartment of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
Yaoqi ZhouInstitute for Glycomics and School of Informatics and Communication Technology, Griffith University, Parklands Dr., Southport, QLD, 4215, Australia.
Yunlong LiuCenter for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 w 10th st, HS 5000, Indianapolis, IN, 46202, USA. yunliu@iu.edu.
Indiana University School of MedicineCardiff University · GBGriffith University · AUHarbin Engineering University · CNHeilongjiang Institute of Technology · CNIndiana University Bloomington · USIndiana University – Purdue University Indianapolis · US

Funding

Research Education ComponentP30AG010133 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI SAYKIN, ANDREW J · 1991 to 2020
$37.3M
NEUROBEHAVIORAL STUDY OF SCHIZOPHRENIAR01MH042191 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI GUR, RAQUEL E · 1987 to 2018
$7.1M
Computational tools for top down mass spectrometry based proteoform identification and proteogenomicsR01GM118470 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Xiaowen Liu · 2016 to 2026
$2.7M
Regulation of mRNA splicing by intronic genetic variantsR01CA213466 · NCI · INDIANA UNIVERSITY INDIANAPOLIS · PI LIU, YUNLONG, SKAAR, TODD C. · 2016 to 2018
$1.7M
Impact of Prenatal Alcohol Exposure on Receptor Targeting and FunctioningR03AA020101 · NIAAA · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI CALDWELL, KEVIN K. · 2010 to 2011
$148k
National High-Tech Research and Development Program 2015AA020101NCI NIH HHS R01 CA213466NIAAA NIH HHS R03 AA020101NIA NIH HHS P30 AG010133NIGMS NIH HHS R01 GM118470NIMH NIH HHS R01 MH042191
6 · The paper itself

Abstract

While synonymous single-nucleotide variants (sSNVs) have largely been unstudied, since they do not alter protein sequence, mounting evidence suggests that they may affect RNA conformation, splicing, and the stability of nascent-mRNAs to promote various diseases. Accurately prioritizing deleterious sSNVs from a pool of neutral ones can significantly improve our ability of selecting functional genetic variants identified from various genome-sequencing projects, and, therefore, advance our understanding of disease etiology. In this study, we develop a computational algorithm to prioritize sSNVs based on their impact on mRNA splicing and protein function. In addition to genomic features that potentially affect splicing regulation, our proposed algorithm also includes dozens structural features that characterize the functions of alternatively spliced exons on protein function. Our systematical evaluation on thousands of sSNVs suggests that several structural features, including intrinsic disorder protein scores, solvent accessible surface areas, protein secondary structures, and known and predicted protein family domains, show significant differences between disease-causing and neutral sSNVs. Our result suggests that the protein structure features offer an added dimension of information while distinguishing disease-causing and neutral synonymous variants. The inclusion of structural features increases the predictive accuracy for functional sSNV prioritization.

Indexed as

AlgorithmsModels, GeneticPolymorphism, Single NucleotideFemaleGenetic Diseases, InbornHumansMaleRNA SplicingPosition Specific Score MatrixPosition Weight MatrixRandom ForestSolvent Accessible Surface AreaSplice Site

Identifiers

PMID28391525
PMCPMC5602096
OpenAlexW2605196967

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.